SAnoVs: Secure Anonymous Voting Scheme for clustered ad hoc networks

In this paper we propose a secure anonymous voting scheme (SAnoVS) for re-clustering in the ad-hoc network. SAnoVS extends our previous work of degree-based clustering algorithms by achieving anonymity and confidentiality of the voting procedure applied to select new cluster heads. The security of SAnoVS is based on the difficulty of computing discrete logarithms over elliptic curves, the intractability of inverting a one-way hash function and the fact that only neighboring nodes contribute to the generation of a shared secret. Furthermore, we achieve anonymity since our scheme does not require any identification information as we make use of a polynomial equation system combined with pseudo-random coordinates. The security analysis of our scheme is demonstrated with several attacks scenarios.examined with several attack scenarios and experimental results

Detection and Prevention System towards the Truth of Convergence on Decision Using Aumann Agreement Theorem

AbstractThe Detection and Prevention system against many attacks has been formulated in Mobile ad hoc networks to secure the data and to provide the uninterrupted service to the legitimate clients. The formulation of opinion of neighbors or belief value or Trust value plays vital role in the detection system to avoid attacks. The attack detection system always extracts the behaviors of nodes to identify the attack patterns and prediction of future attacks. The False positives and false negatives plays vital role on identification of attackers accurately without any false positives and negatives .Our system uses the Aumann agreement theorem for convergence of Truth on opinion based on the bound of confidence value, such that truth consensus will maintained, The accuracy of system will be enhanced through this methodolog

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A Penalty-Based Approach to Handling Cluster Sizing in Mobile Ad Hoc Networks

In Mobile Ad Hoc Networks (MANETs) nodes are allowed to move freely which causes instability in the network. To handle this, the nodes are grouped into clusters which make the topology of the network appear more stable. In proposed algorithms, the size of these clusters has been either ignored or handled insufficiently. This Thesis proposes a penalty-based approach to handle cluster sizing in a more appropriate manner. A configurable penalty function is defined which assigns penalties to each of the possible cluster sizes. The penalty is then used in conjunction with a merge qualifier to determine if a merge is allowed. Merges will be allowed if the total penalty of the two clusters decreases as a result of the merge. Additionally a split merge process has been developed to allow a number of nodes to split from a cluster and merge with a new cluster. A separate split merge qualifier is used to determine if a split merge will be allowed to happen; it will as long as the total penalty of the two clusters after the split merge is less than the total penalty before the split merge. Simulations and thorough analysis of the results show that the proposed changes are on par with the base algorithm used; however, the penalty function allows for a more complex clustering sizing strategy